AUTHOR=Chen Tongzuan , zhao Liqian , Chen Junbo , Jin Gaowei , Huang Qianying , Zhu Ming , Dai Ruixia , Yuan Zhengxi , Chen Junshuo , Tang Mosheng , Chen Tongke , Lin Xiaokun , Ai Weiming , Wu Liang , Chen Xiangjian , Qin Le 

TITLE=Identification of three metabolic subtypes in gastric cancer and the construction of a metabolic pathway-based risk model that predicts the overall survival of GC patients

JOURNAL=Frontiers in Genetics

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1094838

DOI=10.3389/fgene.2023.1094838

ISSN=1664-8021

ABSTRACT=Gastric cancer (GC) has high heterogeneity and poor overall survival (OS). It is also challenging to perform prognostic prediction of GC patients. This is partially because little is known about the prognosis-related metabolic pathways in this disease. Hence, our objective was to identify GC subtypes and genomes related to prognosis, based on the activity alterations of core metabolic pathways among different GC tumor samples. In this study, we uncovered activity differences of metabolic pathways within GC patients, using gene set variation analyses (GSVA), and established three clinical subtypes, using the non-negative matrix factorization (NMF). Based on our analysis, subtype 1 showed great prognosis, while subtype 3 exhibited the worst prognosis. Interestingly, we observed profound differences in the genomic characteristics among the three subtypes, through which we identified a new evolutionary driver gene, CNBD1. Furthermore, we utilized 11 potential metabolic-prognosis gene signatures, obtained from algorithms like Lasso and random forest, to construct a model of prognostication, and verified our results using qRT-PCR (5 matched clinical tissues of GC patients). We demonstrated that this model was both effective and robust in cohorts GSE84437 and GSE26253, and the results from the multivariate cox regression analyses confirmed that the 11 gene-signature was an independent signature for prognostic prediction (P< 0.0001, HR=2.8, 95%CI2.1-3.7). Therefore, this signature was speculated to be relevant to the infiltration of tumor-associated immune cells. Our work identified significant GC prognosis-related metabolic pathways in different GC subtypes, and provided new insights into GC subtype prognostic assessment.